Measuring novel configuration of points

I am trying to implement Novelty search; I understand why it can work better than the standard Genetic Algorithm based solution which just rewards according to the objective. I am working on a problem which requires to generate a fixed number of points in a 2d box centered at the origin. In this problem, how can I identify which is a novel configuration of points?

Note: I have thought of one way of doing this: We call the mean of one configuration of points to be the mean of all points in that configuration (let's say this tuple is $$(m_x, m_y)$$, we store the mean of all configurations generated till now, now for a new configuration it's novelty can be defined as the distance of the mean of this new configuration with $$(m_x, m_y)$$.
But I think it will not work greatly as some very different configuration of points can also have the same mean.

1. Maximum distance of the new point with all points of the configuration ($$M$$)
2. Minimum distance of the new point with all points of the configuration ($$N$$)
3. $$\frac{M}{\text{Maximum distance between two points of the configuration(}D)}$$: normalize (1) measure
4. $$\frac{N}{D}$$:normalize (2) measure