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Is there a way to select the most important features using PCA? I am not looking for the principal components with the highest scores but a subset of the original features.

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There are better methods for selecting most important features in supervised setting. Assuming they are not an option, or you're simply interested in PCA:

Say you originally had 100 features and you applied PCA and first 10 PCs explains the 95 % of ratio.

After applying PCA, you can calculate linear correlations between top 10 PCs and original features. I assume some of your features will be highly correlated with some subset of top 10 PCs. You can draw an abstract line and choose subset of original features that are at least 0.80 linearly correlated with at least one of top 10 PCs.

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