Is there a thumb-rule for designing neural-networks?
This question has been answered in detail on CrossValidated: How to choose the number of hidden layers and nodes in a feedforward neural network?

However, let me add my own two cents:

There is no magic rule for choosing the best neural network architecture, but if you can find an architecture someone has used to solve a similar problem this is often an excellent starting point.

The best places to look are official or unofficial examples using popular neural network libraries  such as Keras, PyTorch, or Tensorflow, and architectures described in academic literature. keras/examples on github is a great resource.

These architectures were likely chosen after lots of trial and error, so most of the work will have been done for you.