Obviously, if the data were normal, we would start with a 2-way ANOVA to look for the group by time interaction. If the data are not too badly non-normal, I might do that anyway. It provides a nice support for the follow-up tests. If the interaction is entirely NS, you have no basis for most of the rest.

To compare across time within the treatment group, I would start with a Friedman ANOVA. This would test the overall pattern for significance. I would also do one within the control group, since there may be spontaneous changes.

For the between group comparisons, I would compare at each time on the change from baseline. Are you doing that? I can't tell.

Since some of these are getting at the question from different angles, I think I would test the over-time analyses at alpha 0.05. You are not expecting any change in the control group (or don't much care about it), so you really have only 1 over time ANOVA you care about.

If the Friedman ANOVAs are significant, I would Bonferroni correct the 3 comparisons to baseline at 0.05/3. Some people would compare every time to every other time, which adds to the denominator! But ask yourself how important those comparisons, even to baseline, are. Say the medians are 10 20 30 40 in the treatment group (p = 0.001 by Friedman) and 10 11 10 11, p = 0.9 in the controls. Do you really need to compare each time to baseline? If the pattern is less clear, then you need some comparisons and p-values...

I would use alpha = 0.05/3 for the comparison of groups on the changes from baseline. I would not include baseline in the correction.

take note that getting a p-value that barely makes significance is now starting to be considered very weak evidence. I hope you have p values of 0.001!