The question in the title is inconsistent with the question body. If you are intent on using a CNN albeit it not being the best idea for this type of data, then for the dataset you are describing a 1D CNN architecture would be most suitable.
To answer the question in the title, yes a 3D CNN is definitely possible and is actually commonly used on 3D data, for example: video (3rd dimension is time), 3D medical images, etc.
The instances in the dataset you described is a vector with 20 instances. This data is a single dimensional structure. You can thus use a 1D CNN as described here.
However, it would be best to use a DNN for such a data structure. As a CNN is essentially cross-correlation of neighboring features. In this case, your data cannot be assumed to have such similarities between adjacent features. A DNN will allow for cross-correlation between distant features to be captured more effectively.