Adding more features in SVC leading to worse performance, even w/ regularization
30 samples probably just isn't enough. The more features you want to use, the more samples you'll need - otherwise you'll get huge model instability and overfitting (especially with bad/useless features). With only 30 samples you'll probably get the "best" results with 1 or 2 carefully selected features. Get 100 or 200 samples, then try again with 5 features.

Also make sure you're standardizing your features - for example by removing the mean and scaling to unit variance. SVMs don't like features that are a lot larger than other features.