Should dimensionality reduction be done before k-means clustering if there are many features?
For the first idea about PCA, you can not simply just use 2 components. You need to take a look at the explained variance by your principal components and based on that you should select the required number of components. If, for example, you found that the first two components explain a significant amount of variance (e.g., more than 95%), then, you can use them to perform k-means clustering. In that case, it is expected (but not for sure and for all cases) that you get the same results as when you perform k-means using all features. 

My suggestion is to use all of your features if you need to consider a large number of components. My reason is that your dataset is too small and it is not computationally demanding to perform k-means clustering using your dataset. 

As a side note, also you can try all of your options because your dataset is small and then you will find out what's going on in your data.