How modelling is affected by similar feature distributions across classes?
EDA
What you are doing there, falls under Exploratory Data Analysis (EDA). A better way to investigate how your features between them and are distributed across classes is a correlogram, sometimes referred to as pairplot.
A correlogram helps with

investigating relationships between pairs of numerical features, via scatter plots for each pair of features
inspecting the distribution of each feature, using a histogram or a density plot in the diagonal of the pairplot.

You can easily create one of those in seaborn:
# library &amp; dataset
import seaborn as sns
import matplotlib.pyplot as plt
df = sns.load_dataset('iris')

# Basic correlogram
sns.pairplot(df, kind=&quot;scatter&quot;)
sns.plt.show()

# Annotate classes with in different colours
sns.pairplot(df, kind=&quot;scatter&quot;, hue=&quot;species&quot;)

# Use regression instead of scatter
sns.pairplot(df, kind=&quot;reg&quot;, hue=&quot;species&quot;)


You can see how classes may or may not form clusters which an algorithm could potentially draw hyperplanes by combining and transforming features.
Your question

Can it mean that the feature isn't that significant to data prediction and can be dropped?

The y axis maximum value differs between the two plots you have posted, and so it is not easy visually inspect fairly.
One could potentially assume that the model may not &quot;work&quot; with that particular feature to begin with, should there be other variables in that dataset that are easier to separate. This assumption however (a) can be considered naïve given how ML algorithms work to transform the feature space in order to make classes separable and therefore (b) does not constitute grounds for dropping this feature.
A less useful feature in your dataset can still increase the performance of your model. In the case of unbearably large feature space you can use dimensionality reduction techniques (PCA, kernel PCA, autoencoders).
Dropping features usually relates to how well these relate to the ground truth e.g. noise, or how they may affect feature importance and model stability e.g. multicollinearities.

Image from: https://stackoverflow.com/questions/59212378/how-do-i-get-the-diagonal-of-sns-pairplot