t-tests or mann-whitney??

I have a n=12 sample (and a matched-sample of 12)... I was planning on using t-tests, but now reconsidering it. Would it be correct to use a mann-whitney test to compare the means of the 2 samples (because of the sample size)?

I did K-S test to check for normality and no-significance was noted- so I'm assuming my data distributions are normal... was this the correct way to check for normal distribution. I tried looking at histograms to eye-ball it, but I don't get it!!

all things equal, are there disadvantages to using Mann-Whitney U rather than t-tests?

I'm trying to figure out the most appropriate way to analyze my data... any advice would be appreciated. Thanks.
From the sounds of it, you at least have the normality assumption satisfied. You should eye-ball the histograms or use Q-Q plots for each group too, however.

What about homogeneity of variances? A Levene's / Bartlett test will let you know if you have heterogeneous variances or not.

Mann-Whitney is a non-parametric alternative to the independent samples t-test. So unless your data is in the form of ranks (i.e, measured on an ordinal scale) or you have severe violations of the assumptions of normality/equality of variances, you should use the T-Test approach.
If you doubt about normality, you can use permutation test. This kind of test doesn't assume normality or independence, just variance homogeniety.
You can find it on package "coin" in R/S-Plus